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Model architecture level privacy leakage in neural networks

Science China(Information Sciences) 2024年第3期67卷 20-32页

作者： Yan LI Hongyang YAN Teng HUANG Zijie PAN Jiewei LAI Xiaoxue ZHANG Kongyang CHEN Jin LI Institute of Artificial Intelligent and Blockchain Guangzhou University Pazhou Lab

Privacy leakage is one of the most critical issues in machine learning and has attracted growing interest for tasks such as demonstrating potential threats in model attacks and creating model *** recent years, numerous studies have revealed various privacy leakage risks(e.g., data reconstruction attack, membership inference attack, backdoor attack, and adversarial attack) and several targeted defense approaches(e.g., data denoising, differential privacy, and data encryption). However, existing solutions generally focus on model parameter levels to disclose(or repair) privacy threats during the model training and/or model interference process, which are rarely applied at the model architecture level. Thus, in this paper, we aim to exploit the potential privacy leakage at the model architecture level through a pioneer study on neural architecture search(NAS) paradigms which serves as a powerful tool to automate a neural network *** investigating the NAS procedure, we discover two attack threats in the model architecture level called the architectural dataset reconstruction attack and the architectural membership inference attack. Our theoretical analysis and experimental evaluation reveal that an attacker may leverage the output architecture of an ongoing NAS paradigm to reconstruct its original training set, or accurately infer the memberships of its training set simply from the model architecture. In this work, we also propose several defense approaches related to these model architecture attacks. We hope our work can highlight the need for greater attention to privacy protection in model architecture levels(e.g., NAS paradigms).

关键词： neural architecture search data reconstruction attack membership inference attack

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